运行日志：
D:\Anaconda3\python.exe
E:/PycharmProject/deeplearning/demo.py
2017-06-19 11:18:46.312109: W
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE instructions, but
these are available on your machine and could speed up CPU
computations.
2017-06-19 11:18:46.312532: W
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE2 instructions,
but these are available on your machine and could speed up CPU
computations.
2017-06-19 11:18:46.312924: W
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE3 instructions,
but these are available on your machine and could speed up CPU
computations.
2017-06-19 11:18:46.313309: W
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE4.1 instructions,
but these are available on your machine and could speed up CPU
computations.
2017-06-19 11:18:46.313686: W
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE4.2 instructions,
but these are available on your machine and could speed up CPU
computations.
2017-06-19 11:18:46.314053: W
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX instructions, but
these are available on your machine and could speed up CPU
computations.
2017-06-19 11:18:46.314408: W
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX2 instructions,
but these are available on your machine and could speed up CPU
computations.
2017-06-19 11:18:46.314767: W
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use FMA instructions, but
these are available on your machine and could speed up CPU
computations.
2017-06-19 11:18:48.199218: I
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:940]
Found device 0 with properties:
name: GeForce 940MX
major: 5 minor: 0 memoryClockRate (GHz) 1.189
pciBusID 0000:01:00.0
Total memory: 4.00GiB
Free memory: 3.36GiB
2017-06-19 11:18:48.199659: I
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:961]
DMA: 0
2017-06-19 11:18:48.199868: I
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:971]
0: Y
2017-06-19 11:18:48.200090: I
c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030]
Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce
940MX, pci bus id: 0000:01:00.0)
[5.0]